Digital computers are being used today to perform a wide variety of tasks. Many different areas of business, industry, government, education, entertainment, and most recently, the home, are tapping into the enormous and rapidly growing list of applications developed for today's increasingly powerful computer devices.
One of the most popular applications of computer systems and computer implemented communication is the rapidly emerging field of electronic business applications, or “E-business” applications. E-business applications operate in a distributed environment involving multiple parties with heterogeneous application servers and systems. It is inappropriate to provide a single, centralized point of control for such applications, since the parties involved typically are independent business entities.
One recent prior art approach for implementing decentralized distributed computer environments involves the use of “peer-to-peer” methods. The peer-to-peer (P2P) interaction paradigm has become popular because it does not require centralized control. Traditionally, the P2P paradigm has been used for sharing resources (e.g., music files) among clients on behalf of individual consumers. Applying the P2P computing paradigm at the business level represents both opportunities and challenges. Many e-business applications (e.g., business intelligence, procurement) require bulk data transfer (large data files, XML documents, multimedia content), but prior art P2P methods have limited capacity and throughput for handling bulk content delivery.
Problems with the popular prior art P2P computing paradigm arise in cases where large amounts of information needs to be exchanged between two or more business entities (e.g., “peers”). For example, in a typical business intelligence service provisioning scenario, a client business contracts with a service provider to analyze large amounts of data collected from the client's operational systems. The objective of the analysis is to generate product recommendation rules, marketing promotions, fraud detection, risk analysis, or some other business intelligence functions. The transaction data, typically maintained in the client's operational databases, are transferred to a data analysis system and then summarized and analyzed using data analysis tools.
The data analysis ultimately results in business guidelines, such as, for example, aggregated information, association rules, promotion plans, fraud detection hints. Very often, such an analytic task is not a single step, but involves multi-step business conversations through message exchanges as well as content delivery between the operational system and the analysis system. Thus, the repeated exchange of large amounts of data between different business entities is not suited for the prior art P2P paradigm. This is especially so in a case where data needs to be exchanged across organizational or enterprise boundaries (e.g., across security barriers and firewalls).
Yet another problem with the use of the prior art P2P paradigm in a business setting is the manageability of peers. For example, in some P2P infrastructures such as Jxta™, peers can form ad hoc peer groups. However, when P2P protocols for inter-enterprise business interactions are applied, peer groups need to be allowed to be defined cognizant of the fact that there are organizational boundaries within enterprises. In the business intelligence service example above, the enterprise can include multiple servers and the organizational elements responsible for collecting client data may need to be separate from the organizational elements responsible for analyzing the client data.
Thus what is required is a solution that efficiently implements P2P collaboration in cases requiring the exchange of large data sets. What is required is a solution that manages data exchange between peers across different organizational or enterprise boundaries and efficiently implements peer management to facilitate business interactions. The present invention provides a novel solution to the above requirements.